Risk Intensity and Exposure-Weighted Burden of Multiple Hazards in Serbia: A Nationwide Secondary Analysis of Public Health Emergency Assessments

Public health emergency risk assessments commonly summarize likelihood and impact, but categorical risk matrices may not fully represent the population-exposure burden created by multi-hazard portfolios. This nationwide ecological study analyzed municipality-level summaries from 161 Serbian local administrative units assessed with the World Health Organization Strategic Toolkit for Assessing Risks (WHO STAR) in 2024. In addition to the supplied ordinal STAR measures, we derived an analytical probability-impact product (PIP), cumulative hazard-exposure burden (CHEB = hazard count × exposure), portfolio risk burden (PRB = PIP × CHEB), and a sample-relative 0–100 Integrated Portfolio Disaster Risk Index (IPDRI). Geographic Information Systems (GIS)-based thematic mapping was used to integrate the analytical dataset with local administrative boundaries and to visualize the spatial distribution of the original STAR indicators and derived burden measures. Primary inference combined rank-based methods with Pearson correlations, Welch tests, robust analysis of variance, non-circular sensitivity models, and exploratory cluster analysis. The supplied STAR risk distribution was predominantly moderate (83.2%), with 11.2% low and 5.6% high. Risk was most strongly associated with probability (Spearman’s ρ = 0.534, 95% bootstrap CI [0.357, 0.683], p < 0.001) and frequency (ρ = 0.360, p < 0.001), and differed substantially across 25 districts, H(24) = 81.95, p < 0.001, ε² = 0.426. Applying STAR matrix thresholds to aggregate PIP reproduced the supplied risk category in 75.8% of units (quadratic weighted κ = 0.484), indicating only moderate aggregate concordance. IPDRI averaged 43.64 (SD = 21.45) and was strongly associated with CHEB (r = 0.889, 95% CI [0.852, 0.918], p < 0.001), but not significantly associated with the supplied STAR risk category (r = 0.117, p = 0.138). IPDRI differed across macroregions, Welch F(2, 53.15) = 10.11, p < 0.001, and districts, Welch F(24, 38.79) = 14.12, p < 0.001. A three-cluster solution (silhouette = 0.549) identified distinct local risk profiles and differed on external STAR risk, F(2, 158) = 12.72, p < 0.001, η² = 0.139. The findings indicate that STAR risk intensity and exposure-weighted portfolio burden capture complementary dimensions of local public health emergency risk and should be interpreted jointly in preparedness planning.

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Publication Details

Journal
Safety
Published
2026-11-04
DOI
https://doi.org/10.3390/safety12060145
Primary Topic
Disaster Management and Resilience
Type
article
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article

Risk Intensity and Exposure-Weighted Burden of Multiple Hazards in Serbia: A Nationwide Secondary Analysis of Public Health Emergency Assessments

Jasmina Bašić, Vladimir Miroljub Cvetković, Velibor Lalić, Tin Lukić et al.
Safety
Disaster Management and Resilience
article

Risk Intensity and Exposure-Weighted Burden of Multiple Hazards in Serbia: A Nationwide Secondary Analysis of Public Health Emergency Assessments

Jasmina Bašić, Vladimir Miroljub Cvetković, Velibor Lalić, Tin Lukić, Luka Sablјić, Vanja D. Cvetković, Dejan Ivanović
article en

Abstract

Public health emergency risk assessments commonly summarize likelihood and impact, but categorical risk matrices may not fully represent the population-exposure burden created by multi-hazard portfolios. This nationwide ecological study analyzed municipality-level summaries from 161 Serbian local administrative units assessed with the World Health Organization Strategic Toolkit for Assessing Risks (WHO STAR) in 2024. In addition to the supplied ordinal STAR measures, we derived an analytical probability-impact product (PIP), cumulative hazard-exposure burden (CHEB = hazard count × exposure), portfolio risk burden (PRB = PIP × CHEB), and a sample-relative 0–100 Integrated Portfolio Disaster Risk Index (IPDRI). Geographic Information Systems (GIS)-based thematic mapping was used to integrate the analytical dataset with local administrative boundaries and to visualize the spatial distribution of the original STAR indicators and derived burden measures. Primary inference combined rank-based methods with Pearson correlations, Welch tests, robust analysis of variance, non-circular sensitivity models, and exploratory cluster analysis. The supplied STAR risk distribution was predominantly moderate (83.2%), with 11.2% low and 5.6% high. Risk was most strongly associated with probability (Spearman’s ρ = 0.534, 95% bootstrap CI [0.357, 0.683], p < 0.001) and frequency (ρ = 0.360, p < 0.001), and differed substantially across 25 districts, H(24) = 81.95, p < 0.001, ε² = 0.426. Applying STAR matrix thresholds to aggregate PIP reproduced the supplied risk category in 75.8% of units (quadratic weighted κ = 0.484), indicating only moderate aggregate concordance. IPDRI averaged 43.64 (SD = 21.45) and was strongly associated with CHEB (r = 0.889, 95% CI [0.852, 0.918], p < 0.001), but not significantly associated with the supplied STAR risk category (r = 0.117, p = 0.138). IPDRI differed across macroregions, Welch F(2, 53.15) = 10.11, p < 0.001, and districts, Welch F(24, 38.79) = 14.12, p < 0.001. A three-cluster solution (silhouette = 0.549) identified distinct local risk profiles and differed on external STAR risk, F(2, 158) = 12.72, p < 0.001, η² = 0.139. The findings indicate that STAR risk intensity and exposure-weighted portfolio burden capture complementary dimensions of local public health emergency risk and should be interpreted jointly in preparedness planning.

SafetyVol. 12(6)
Good health and well-being, Sustainable cities and communities
Openalex Percentile: Top 33%
Disaster Management and Resilience
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